解决多用户语义通信中的表征不一致问题，提升协作效率。
Federated Latent Space Alignment for Multi-user Semantic Communications
- 通过联邦优化在基站与用户端协同训练语义均衡器。
- 实测表明该方法在精度与通信开销间取得良好平衡。
- 适合资源受限的AI原生多设备协同场景。
语义通信旨在高效完成任务，但智能设备间不同的潜在表征会导致语义错配，阻碍相互理解。本文提出一种新方法，缓解多智能体AI原生语义通信中的潜在空间错位问题。在下行链路场景中，接入点（AP）需与多个用户协作完成特定人工智能驱动任务。所提方法在AP部署语义预均衡器，在用户端部署本地语义均衡器，促进互信与任务导向通信，同时考虑功率和复杂度约束。通过联邦优化实现AP与用户侧语义均衡器的分布式训练。数值结果验证了该方法在目标导向语义通信中的有效性，揭示了精度、通信开销、复杂度及设备语义相近性之间的关键权衡。
原文摘要 · Abstract (English)
Semantic communication aims to convey meaning for effective task execution, but differing latent representations in AI-native devices can cause semantic mismatches that hinder mutual understanding. This paper introduces a novel approach to mitigating latent space misalignment in multi-agent AI- native semantic communications. In a downlink scenario, we consider an access point (AP) communicating with multiple users to accomplish a specific AI-driven task. Our method implements a protocol that shares a semantic pre-equalizer at the AP and local semantic equalizers at user devices, fostering mutual understanding and task-oriented communication while considering power and complexity constraints. To achieve this, we employ a federated optimization for the decentralized training of the semantic equalizers at the AP and user sides. Numerical results validate the proposed approach in goal-oriented semantic communication, revealing key trade-offs among accuracy, com- munication overhead, complexity, and the semantic proximity of AI-native communication devices.
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